Multi-axis and cornering stiffness properties of non-pneumatic wheels with symmetric helical honeycomb spokes
Bibliographic record
Abstract
Current non-pneumatic wheel (NPW) designs with non-helical honeycomb spokes generally show too high cornering stiffness and thereby rapid saturation of the cornering force, which may lead to inferior handling and directional control of the vehicle, particularly under high operating speeds. The design concept of NPWs with symmetric helical honeycomb spokes is thus proposed in this paper. Three-dimensional (3D) finite element (FE) models of a honeycomb NPW with symmetric helical spokes configurations of different cell and helix angles were developed in order to fundamentally study its multi-axis and cornering stiffness properties under a constant normal load. The validities of the developed NPW models with 0° helix angle and three different cell angles were demonstrated through comparisons of predicted wheel responses with results available in published studies. 3D simulations were conducted under two design constraints in terms of identical cell-wall thickness and identical load carrying capacity. The results suggest that increasing helix angle results in significantly greater in-plane shear stiffness of the honeycomb spokes and thus could effectively yield higher longitudinal and vertical stiffness of the NPW. An increase in helix angle also causes lower lateral stiffness for the wheel designs with 15.8° and 31.5° cell angles resulting from increases in the out-of-plane compliance of the spokes, apart from the notably lower cornering stiffness, particularly when it is increased to 30° and 45°. The cell-wall thickness, however, shows positive influences on multi-axis stiffness of the honeycomb wheel but negative effects on its cornering stiffness. The design concept of helical honeycomb spokes could offer better vehicle performances than the current designs in terms of braking/traction and handling characteristics. These are particularly important for promoting applications of the NPW in high-speed vehicles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".